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Record W2547687385 · doi:10.21273/hortsci.35.3.473e

463 Keeping in Touch While Studying Abroad

2000· article· en· W2547687385 on OpenAlexaboutno aff
Tim Rhodus

Bibliographic record

VenueHortScience · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsTRIPS architectureStudy abroadQuarter (Canadian coin)Variety (cybernetics)Class (philosophy)InstitutionMedical educationAcademic institutionPsychologyPublic relationsPedagogySociologyPolitical scienceEngineeringLibrary scienceGeographyMedicineComputer scienceSocial scienceTransport engineering

Abstract

fetched live from OpenAlex

Study Abroad programs are designed to provide a variety of learning opportunities for students. Experiencing firsthand the culture, environment, and/or industry is often described as the most memorable benefit by those who study for a quarter or semester in another country. Unfortunately, it is difficult to share this learning experience with classmates and family members who are back at home. One solution that has been implemented with the College's Study Abroad program at The Ohio State Univ., is to design a web site that chronicles the experiences and activities of students while they are abroad. In addition to the photos and stories being contributed from abroad, classmates and other individuals from the home institution can submit questions and participate in threaded discussions with those abroad. For example, students at home can post questions regarding an upcoming tour location and utilize the responses and photos for a class they are attending. Finally, being able to review experiences from previous trips is an outstanding strategy for promoting the program to new students. Online experiences from the Dominican Republic and England programs are available at: http://cfaes.ohio-state.edu/studyabroad .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0430.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.342
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2000
Admission routes1
Has abstractyes

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